yjh0410 49b72e2035 RandomJitterCrop		1 년 전
..
basic_modules	49b72e2035 RandomJitterCrop	1 년 전
README.md	ddf611a65e update	1 년 전
build.py	9472637cf3 modify train code	1 년 전
loss.py	0b1cbf787c fix a bug in num_gts	1 년 전
loss_utils.py	7211aab24e fix a bug in num_gts	1 년 전
matcher.py	b6a1ff72a3 train RT-DETR-R18 on COCO	1 년 전
rtdetr.py	a716a66687 add RandomExpand into RT-DETR's augmentation	1 년 전
rtdetr_decoder.py	24de483708 modify TAL	1 년 전
rtdetr_encoder.py	c7174f216d complete rtdetr model	1 년 전

Real-time Transformer-based Object Detector:

This model is not yet complete.

Results on the COCO-val

For the backbone of the image encoder, we use the IN-1K classification pretrained weight. It might be hard to train RT-DETR from scratch without IN-1K pretrained weight.
For training, we train RT-DETR series with 6x (~72 epochs) schedule on COCO.
For data augmentation, we use the color jitter, random hflip, random crop, and multi-scale training trick.
For optimizer, we use AdamW with weight decay 0.0001 and base per image lr 0.001 / 16.
For learning rate scheduler, we use cosine decay scheduler.

Train RT-DETR

Single GPU

Taking training RT-DETR-R18 on COCO as the example,

python train.py --cuda -d coco --root path/to/coco -m rtdetr_r18 -bs 16 -size 640 --max_epoch 72 --eval_epoch 5 --no_aug_epoch -1 --ema --fp16 --multi_scale

Multi GPU

Taking training RT-DETR-R18 on COCO as the example,

python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m rtdetr_r18 -bs 16 -size 640 --max_epoch 72 --eval_epoch 5 --no_aug_epoch -1 --ema --fp16 --sybn --multi_scale --save_folder weights/

Test RT-DETR

Taking testing RT-DETR-R18 on COCO-val as the example,

python test.py --cuda -d coco --root path/to/coco -m rtdetr_r18 --weight path/to/rtdetr_r18.pth -size 640 -vt 0.4 --show

Evaluate RT-DETR

Taking evaluating RT-DETR-R18 on COCO-val as the example,

python eval.py --cuda -d coco-val --root path/to/coco -m rtdetr_r18 --weight path/to/rtdetr_r18.pth

Demo

Detect with Image

python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m rtdetr_r18 --weight path/to/weight -size 640 -vt 0.4 --show

Detect with Video

python demo.py --mode video --path_to_vid path/to/video --cuda -m rtdetr_r18 --weight path/to/weight -size 640 -vt 0.4 --show --gif

Detect with Camera

python demo.py --mode camera --cuda -m rtdetr_r18 --weight path/to/weight -size 640 -vt 0.4 --show --gif

Model	Batch	Scale
RT-DETR-R18	2xb8	640
RT-DETR-R50	2xb8	640
RT-DETR-R101	2xb8	640

README.md